Embed Size (px)
Citation preview
70
Attitude, Behavioral Intention and Usage: An Empirical Study of Taiwan
Railway’s Internet Ticketing System
Wen-Hung Wang
Department of Shipping and Transportation Management
National Taiwan Ocean University, Assistant Professor
Email: [email protected]
Tel: 886+2+24622192 ext. 3430
Fax: 886+2+24631903
Address: No.2, Beining Rd., Jhongjheng District, Keelung City 202, Taiwan (R.O.C.)
Yi-Jyun Liu
Department of Shipping and Transportation Management
National Taiwan Ocean University
Email: [email protected]
ABSTRACT
The Internet has become an essential part of business due to its rapid growth. Many
businesses circulate information and interact with potential customers through the Internet,
while some also trade with customers on their websites. Furthermore, many studies have
observed that a website is an essential component of business, and websites are becoming
increasingly important. Based on TAM (Davis,1986), this study discusses customer behavior
when using Taiwan Railway’s Internet Ticketing System. Hee-dong and Youngjin (2004) have
observed that “…attitude deserves more attention in IS research due to its considerable
influence on individual and organizational usage of IS.”, and discovered that a better
understanding of the role of attitude can enhance explanatory power of the model. Cheng,
Lam, and Yeung (2006) also observed that web security is an important variable in the TAM
model. This study discusses the relationships among web security, attitude, and behavioral
intention. Davis et al.’s technology model (TAM) is used with attitude toward IS usage
described in terms of both the affective and cognitive dimensions.
INTRODUCTION
The theory of reasoned action, TRA (Ajzen et al., 1975) measures the ability to predict
people’s computer acceptance from measuring the effect of perceived and other affective
variables. The technology acceptance model, TAM (Davis, 1986), is based on TRA. However,
some researchers consider that TAM could be expanded as a complete model.
Cheng et al. (2006) concluded that web security has a positive influence on attitude and
behavioral intention. Therefore, this study sets web security as an important variable. Besides,
perceived ease of use and perceived usefulness, which are the two important considerations
affecting the usage of information systems. Some investigations on social psychology have
71
concluded that attitude can be measured in terms of both affective and cognitive dimensions.
Hee-dong et al. (2004) expanded the TAM model with affective attitude and cognitive
attitude as the alternatives to attitude. There, this case study combines these two factors into
the TAM model.
Individuals are increasingly ordering tickets on line. The online ticket in North America
had a value of US$5,400 million in 2004, according to a research report by Ching-Hong Li
from the Institute for Information Industry in Taiwan, based on Forrester’s research reports.
Online ticket sales will continue rising in the next five years, totaling US$9,400 million U.S.
dollars in 2010, according to Forrester’s forecast. Over one-third of online shoppers bought
tickets on line in 2004. Furthermore, these online tickets shoppers bought most show and
sport tickets. Accordingly, this study has the following four objectives:
(1) to present the results of an empirical study of customer attitudes to Taiwan Railway’s
Internet Ticketing System;
(2) to summarize customer attitudes toward behavioral intention and usage in Taiwan
Railway’s Internet Ticketing System, and
(3) to report the effect of web security toward attitude and behavioral intention.
OVERVIEW OF THE FRAMEWORK
Hee-dong et al. (2004) developed a conceptual model of both the affective and cognitive
dimensions of attitude toward IS usage. Their model was based on the original TAM model
(Davis et al., 1989), with the addition of behavioral intention back as a mediator between
attitude and usage.
Cheng et al. (2006) extended the TAM model to include perceived web security as a
predictor of attitude and intention to use.
This study develops a theoretical model including both the concepts included in the
above two works. Figure 1 graphically displays the proposed theoretical model.
Figure 1. Conceptual framework
Perceived ease of use exerts a positive effect on perceived usefulness. An empirical test
for the original TAM model has found that perceived ease of use has a positive influence on
perceived usefulness. The conceptual model of Hee-dong et al. (2004) and the theoretical
72
model of Walczuch, Lemmink and Streukens (2007) indicate that users perform well in tasks
when they do not need to expend much effort. Therefore, hypothesis H1 is proposed as
follows:
H1: Perceived ease of use positively influences perceived usefulness.
Attitude is an essential factor in explaining human behavior. How to measure attitude is
also an interesting issues. Several social psychology studies have concluded that attitude has
both affective and cognitive components (Hee-Dong et al., 2004). Petty et al. (1998) stated,
“The most common classification for the basis of attitude is affect and cognition.” Therefore,
according to Hee-dong et al. (2004), “The dyadic view presumes the affective and cognitive to
be independent variables that affect behavioral intention.” Namely, the conceptual model of
Hee-dong et al. (2004) indicates that both perceived ease of use and perceived usefulness
may affect cognitive and affective attitude, leading to the following hypotheses:
H2: Perceived ease of use positively influences cognitive attitude.
H3: Perceived ease of use positively influences affective attitude.
H4: Perceived usefulness positively influences cognitive attitude.
H5: Perceived usefulness positively influences affective attitude.
Cheng et al. (2006) concluded that web security directly influences attitude and
behavioral intention. Thus, using cognitive and affective attitude to measure attitude leads to
the following hypotheses:
H6: Web security positively influences cognitive attitude.
H7: Web security positively influences affective attitude.
H8: Web security positively influences behavioral intention.
Cognitive attitude also exerts a positive impact on affective attitude. The empirical test
of Hee-Dong et al. (2004)’s found support for a positive influence of cognitive attitude on
affective attitude. Hence:
H9: Cognitive attitude positively influences affective attitude.
Attitude may also have an effect beyond a direct impact on intention. The original TAM
model (Davis, 1986), and the models of Taylor & Todd (1995) and Morris & Dillon (1997)
indicate that attitude exerts a positive effect on behavioral intention. The associated
hypotheses are:
H10: Cognitive attitude positively influences behavioral intention toward usage.
H11: Affective attitude positively influences behavioral intention toward usage.
Finally, since this study is exploring the intentions of using the information technology
system, the condition of actual usage is also of interest. The original TAM model (Davis,
1986) indicates that behavioral intention exerts a positive effect on usage. Bagozzi et al.
(1992), Szajna (1996) and Morris et al. (1997) found that behavioral intention might
influence usage. These observations lead to the following hypothesis:
H12: Behavioral intention positively influences usage.
73
METHODOLOGY
Study object and sample
This study observed the effect of attitude on behavioral intentions of the technology
acceptance model of an information system, based on an internet ticketing system for public
transportation in Taiwan. The railway network in Taiwan was built from 1887, and developed
from steam, diesel and modern electric trains. At the end of 2007, Taiwan Railway had more
than 13,000 employees, and an average of about 465,000 passengers per day. The passenger
ratio per train was about 60% in 2007. Taiwan Railway handles freight as well as passenger
transport as well. The sample system, Taiwan Railway’s internet ticketing system, was
introduced in 1997, with an English-language version being launched in 1998. The system
offers Internet ticketing service, including booking tickets, canceling tickets and looking up
for the available tickets.
According to Hee-dong et al. (2004), attitude is an important factor in IS research, since
it significantly influences individual and organizational usage of IS. Cheng et al. (2006)
concluded that web security should be incorporated into the TAM model. This study explores
how both attitude and web security affect TAM. To explore thoroughly the heterogeneous
characteristics of TAM, data for the present study were collected from Internet and street
surveys. In total, 305 surveys were collected from June 2008 to July 2008. The final sample
of valid questionnaires, after discarding the uncompleted questionnaires, was 296. To avoid
demand effect, the respondents were guaranteed that all answers would be anonymous. Table
1 presents the descriptive statistics analysis of sample size.
Table 1
Descriptive Statistics Analysis of Sample Size
Respondents’ characteristics N=296 Gender Male 33.4％ Female 66.6％ Age
≦20 7.40％ 21 – 30 55.7％ 31 - 40 24.3％ 41 – 50 8.10％ ≧51 4.40％ Education
≦High school diploma 0.70％ Senior high school 13.5％ College 15.9％ University 39.2％ ≧Graduate school 30.7％ Occupation
Information 2.70％ Electronic 2.70％ Service 35.1％ Manufacture 1.00％ Travel 1.40％
74
Development of Measures
Tables 2 and 3 list the items related to all variables. The measures of each construct are
defined as follows.
Table 2
The Survey Instrument-- Exogenous constructs
Items
Item-Construct Loading Cronbach’s
Alpha
Average
Variance
Extracted Standardized t-statistic
Perceived Ease of Use 0.937 0.83
1. Learning to operate Taiwan railway’s
internet ticketing system is easy for me 0.89 19.34
2. I find it easy to get Taiwan railway’s internet
ticketing system to order tickets 0.90 19.70
3. It would be easy for me to become skillful at
using Taiwan railway’s internet ticketing
system
0.92 20.56
4. I would find Taiwan railway’s internet
ticketing system easy to use 0.92 20.54
Web Security 0.940 0.87
1. I feel secure sending personal/ financial info
across the Web 0.96 22.01
2. I feel safe providing personal/ financial info
about me to Taiwan railway’s internet 0.96 21.14
Mass communication 2.00％ Student 33.8％ Other 21.3％ Per Capita Disposable Income/month
≦15,000 33.8％ 15,000~35,000 35.5％ 35,000~55,000 19.6％ 55,000~75,000 6.80％ 75,000~100,000 1.70％ ≧100,000 2.70％ Internet experience
≦3 years 8.10％
3 - 6 years 24.0％
6 - 9 years 29.1％
≧9 years 38.9％
75
ticketing system
3. Web is safe environment to provide personal/
financial info 0.90 19.96
Table 3
The Survey Instrument-- Endogenous constructs
Items Item-Construct Loading Cronbach’s
Alpha
Average
Variance
Extracted Standardized t-statistic
Perceived Usefulness 0.903 0.75
1. Using Taiwan railway’s internet ticketing
system would increase the convenience of
internet ticketing
0.84 --
2. Using Taiwan railway’s internet ticketing
system would improve the efficiency of
internet ticketing
0.90 20.30
3. Using Taiwan railway’s internet ticketing
system would make me get the tickets faster 0.83 17.60
4. I would find Taiwan railway’s internet
ticketing system useful for internet ticketing 0.88 19.58
Cognitive Attitude 0.937 0.82
1. Taiwan railway’s internet ticketing system
is a wise instrument for ordering ticket over
the internet
0.86 --
2. Taiwan railway’s internet ticketing system
is a beneficial instrument for ordering ticket
over the internet
0.93 23.26
3. Taiwan railway’s internet ticketing system
is a valuable instrument for ordering ticket
over the internet
0.92 22.56
Affective Attitude 0.880 0.79
1. Using Taiwan railway’s internet ticketing
system makes me feel happy 0.81 --
2. Using Taiwan railway’s internet ticketing
system makes me feel positive 0.92 19.10
3. Using Taiwan railway’s internet ticketing
system makes me feel good 0.93 19.11
Behavioral Intention 0.719 0.35
76
1. I predict that I will use Taiwan railway’s
internet ticketing system on a regular basis
in the future
0.61 --
2. Although I will likely take public transport
for a long way, I think that I may not take
the train but have the alternatives of other
transport in the future
-0.30 -4.38
3. I expect that I will use Taiwan railway’s
internet ticketing system, or a similar type
of system for ordering tickets over the
internet
0.76 9.06
Usage 0.902 0.88
1. Frequency (per year) 0.87 --
2. Accumulated used times 1.04 31.84
3. Accumulated dollars amount 0.90 23.95
Perceived ease of use: defined as the degree to which an individual believes that learning
to adopt a technology requires little effort (Davis, 1989). Perceptions of whether the Taiwan
Railway internet ticketing system was easy to use were captured using a questionnaire with
four items scored using a 5-point Likert-type scale (1, strongly disagree to 5, strongly agree).
The reliability of the four items in this questionnaire was 0.94, in the reasonable range.
Perceived usefulness: defined as an individual’s perception that use of technology will
improve performance (Davis, 1989). People’s perception that Taiwan Railway’s internet
ticketing system was valuable were captured by a questionnaire with four items scored using
a 5-point Likert-type scale (1, strongly disagree to 5, strongly agree). The reliability of the
four items in this questionnaire was 0.90, in the reasonable range.
Web security: defined as the belief that the Web is secure for transmitting sensitive
information (e.g. credit card or social security number) (Salisbury et al., 2001). This factor
was captured by a three-item questionnaire, again scored using 5-point Likert-type scale (1,
strongly disagree to 5, strongly agree). The reliability of these three items was 0.94, in the
reasonable range.
Cognitive attitude: defined as an individual’s specific beliefs related to the object. This
factor consists of the evaluation, judgment, reception and perception of the object of thought
based on values (Hee-dong et al., 2004). Again, a three-item questionnaire scored using a
5-point Likert-type scale (1, strongly disagree to 5, strongly agree) to assess cognitive
attitudes towards using Taiwan Railway’s internet ticketing system. The reliability of these
three items was 0.94, in the reasonable range.
Affective attitude: this is defined as on the degree of emotional attraction toward an
object (Hee-dong et al., 2004). A three-item questionnaire scored using 5-point Likert-type
scale (1, strongly disagree to 5, strongly agree) was applied to assess affective attitudes
among participants towards using Taiwan Railway’s internet ticketing system. The reliability
77
of these three items was 0.88, in the reasonable range.
Behavioral intention: this is a predictor of use (Venkatesh et al., 2003; Thompson et al.,
2006). A three-item 5-point Likert-type scale questionnaire (1, strongly disagree to 5, strongly
agree) was again used to assess people’s behavioral intention towards using the internet
ticketing system of Taiwan Railway. The reliability of these three items was 0.72, in the
reasonable range.
Usage: this refers to actual behaviors of people using the Taiwan Railway internet
ticketing system. The study incorporated three usage variables, namely frequency of use, total
number of times used and total amount of money spent. Users were asked to indicate how
often they used Taiwan Railway’s internet ticketing system, how many times they had used
Taiwan Railway’s internet ticketing system, and how much money they had spent. The
reliability of these three items was 0.90, in the reasonable range.
Data analysis method and examination
First, in the data examination process we deleted cases incorporating missing values.
Besides, with respect to sample size, it is generally accepted that the minimal sample size
needed to ensure appropriate use of maximum likelihood estimation is 100-150 (Anderson
and Gerbing, 1988). In the present study, we have used somehow a larger sample sizes given
the risk of moderate normality violations. Finally, we tested for the existence of univariate
and multivariate outliers, and no outliers were found in our analyses.
Based on Anderson and Gerbing (1988)’s study, our structural equation model could be
test by a two-stage structural equation model. First, we use confirmatory factor analysis, CFA,
to evaluate construct validity regarding convergent and discriminate validity. Then, the
second is that using path analysis to test the research hypotheses empirically. The
path-analytic procedure is becoming common in studies (Li and Calantone, 1998; Chaudhuri
and Holbrook, 2001).
Overall model evaluation
Table4 is the resulting values of the fit statistics. The χ2 /df=4.16<5 is acceptable
(Wheaton, 1987；Hair et al., 1998). Besides, the values for NFI=0.93, NNFI=0.94>0.9 are
acceptably close to the standards suggested by Tucker and Lewis (1973) & Bentler and
Bonnett (1980); RMSEA, root mean square error of approximation is 0.093<0.1 (Browne and
Cudeck, 1993), and SRMR, standardized root mean residual is 0.05<0.1, are acceptable as
well (Hu and Bentler, 1999). Given that our study performed overall goodness-of-fit indices
were acceptable, our model was developed on theoretical bases. Thus, we can proceed in
evaluating the measurement and structural models.
Table 4
Goodness of fit statistics
Model/Construct χ2 /df GFI RMSEA NFI NNFI SRMR CFI
CFA 4.16 0.82 0.093 0.93 0.94 0.05 0.95
Suggested Values <5 >0.8 <0.1 >0.9 >0.9 <0.1 >0.9
78
Measurement model evaluation
We assessed the quality and adequacy of our measurement models by investigating
convergent validity, and reliability. First, convergent validity was supported as a result of the
fact that the overall fit of the model was good, that all loadings were highly statistically
significant (Hildebrandt, 1987; Steenkamp and van Trijp, 1991). Second, reliability was
supported as a result of the fact that all Cronbach alpha’s values exceeded 0.70, indicating
acceptable reliability levels (Nunnally, 1978).
According to the result of Table3, almost all of the composite reliability measures are
equal to or above 0.60, corresponding to Bagozzi and Yi (1988)’s minimum values of 0.60.
Thus, we could conclude that all constructs yield satisfactory reliabilities. Therefore, these
results have showed that the data is reasonably fit the model.
Path model and hypothesis testing
Table 5 presents the results of the research hypotheses of our structural equation model,
after completing path analysis. Obviously, hypotheses H2, H3 and H8 are not supported. Thus,
perceived ease of use has no significance impact on cognitive attitude; perceived ease of use
has no significance influence on affective attitude, and web security exerts no significant
influence on behavioral intention.
Table 5
Empirical Results of the Proposed Model
Causal Path Hypothesis Expected
Sign
Path
Coefficient t-value
Assessment
(p≦.05)
Perceived ease of use→ Perceived usefulness H 1 + 0.74 12.45 s.
Perceived ease of use→ Cognitive attitude H 2 + 0.02 0.28 n.s.
Perceived ease of use→ Affective attitude H 3 + -0.08 -1.02 n.s.
Perceived usefulness→ Cognitive attitude H 4 + 0.68 8.48 s.
Perceived usefulness→ Affective attitude H 5 + 0.19 2.01 s.
Web security→ Cognitive attitude H 6 + 0.12 2.56 s.
Web security→ Affective attitude H 7 + 0.13 2.51 s.
Web security→ Behavioral intention H 8 + 0.06 1.10 n.s.
Cognitive attitude→ Affective attitude H 9 + 6.92 6.92 s.
Cognitive attitude→ Behavioral intention H 10 + 0.69 7.12 s
Affective attitude→ Behavioral intention H 11 + 0.16 2.05 s
Behavioral intention→ Usage H 12 + 0.13 2.22 s
Note: χ2(217)=967.67, p=0.00, RMSEA=0.098; GFI=0.8, AGFI=0.75; CFI=0.94; NFI=0.93; NNFI=0.93
RESULTS AND DISCUSSION
Conclusions and Managerial Implications
The objective of this case study was to explore the effect of web security and attitude on
the different two dimensions on technology acceptance model. Figure 2 illustrates the results
79
of the hypothesized framework, indicating support for most of the hypotheses tested.
Obviously, from these results, perceived ease of use has no significant impact on cognitive or
affective attitude, while web security has no significant impact on behavioral intention.
Figure 2. Results of hypothesized framework
The results of this case study demonstrate that both cognitive and affective attitude
positive influence behavioral intention, and that behavioral intention positively influences
usage. This finding is obviously different from that of Hee-dong et al. (2004), who concluded
that “affective attitude does not mediate the relationship between cognitive attitude and IS
use.” However, in this case study, the beta coefficient from cognitive attitude to behavioral
intention toward usage was more than four times the value of the beta coefficient from
affective attitude to behavioral intention toward usage. Thus, this case study disagrees with
with Hee-dong et al. (2004), who found that, “The cognitive dimension of attitude played an
important role in explaining IS use.”
Our analytical results also demonstrate that web security exerts a positive effect on
cognitive and affective attitude, but no significance effect on behavioral intention. Obviously,
this finding reveals that attitude is an important factor in behavioral intention toward usage.
For “Both the TAM and TRA models postulate that attitude is determined by one's relevant
beliefs …” (Cheng et al., 2006), this study finds that web security is an important variable
related to belief. However, this finding is inconsistent with that of Cheng et al. (2006)’s study.
The Cheng et al. (2006) the conceptual model, incorporating web security into TAM, had a
positive effect on behavior, but no significant effect on attitude.
Thus, this study provides support for TAM, and confirms the hypothesis that the
cognitive attitude is more powerful than affective attitude in explaining the behavioral
intension toward usage. Besides, our study confirmed Hee-dong et al.’s (2004) argument
about the finding of Davis et al. (1989) that attitude contribute little value toward the usage of
information system for using the mixed measure of the attitude construct.
The following recommendations for the Taiwan Railway Administration can be made
from this study. Our results suggest that attitude influences people’s intention toward
information usage. Thus, improving cognitive attitudes of users would improve people’s
intention to use information usage. The results of this study demonstrate that improving
perceived usefulness and web security would directly improve user attitudes, and that
80
perceived ease of use indirectly affects attitude via perceived usefulness.
Limitations and recommendations for further research
This empirical study was performed with a time constraint, making a large sample
difficult to obtain. Like any cross-sectional studies, this study has limitations. This
cross-sectional study was conducted in Taiwan, and might be difficult to generalize in a rapid
changing world. Addition, more than half of the respondents of this questionnaire had no
experience in using Taiwan Railway’s internet ticketing system. Accordingly, the further
studies could be performed to enlarge the sampling size. Finally, some the research time
period may have been a factor affecting user acceptance of the information system
(Karahanna et al., 1999). Therefore, we recommend the further studies involve a long time
period.
REFERENCE
Anderson, J. C. and Gerbing, D.W. (1988). Structural equation modeling in practice: are
view an recommended two-stepapproach. PsychologicalBulletin, 103(3), 411
Bagozzi, R. P. and Yi, Y. (1988). On the evaluation of structural equation models. Journal of
the Academy of Marketing Science, 16(1), 74-94
Bentler, P. M. and Bonnett, D. G. (1980). Significant tests and goodness of fit in the analysis
of covariance structure. Psychological Bulletin, 88, 588-606
Browne, M. W., and Cudeck, R. (1993). Alternative ways of assessing model fit in Testing
structural equation models. Kenneth B., and Long, J. S. (eds.), Sage, Publications,
Newbury Park, CA
B. Szajna (1996). Empirical evaluation of the revised technology acceptance model.
Management Science, 42(1), 85-92
Chaudhuri, A. and Holbrook, M. B. (2001). The chain of effects from brand trust and brand
affect to brand performance: the role of brand intentions. Journal of Marketing,
65(2), 83-93
Davis, F. D. (1986). A Technology Acceptance Model for Empirically Testing New End-user
Information System: Theory and Results, Doctoral Dissertation, MIT Sloan School
of Management Cambridge, MA
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS Quarterly, 13(3), 319-340
Hair, J. F., Anderson, R. E., Tatham, R. L. and Black W. C. (1998). Multivariate Data
Analysis, New Jersey: Prentice-Hall
Hee-dong Y. and Youngjin Y. (2004). It’s all about attitude: revisiting the technology
acceptance model. Decision Support Systems, 38, 19-31
Hildebrandt, L. (1987). Consumer retail satisfaction in rural areas: are analysis of survey data.
Journal of Economic Psychology, 8(1), 19-42
Hu, L.-T. and Bentler, P. M. (1999). Cut off criteria for fit indexes in covariance structure
81
analysis: conventional criteria versus new alternatives. Structural Equation
Modeling, 6(1), 1-55
Karahanna, E., D. W. Straub, and Norman, L. C. (1999a). Information technology adoption
across time: A cross-sectional comparison of pre-adoption and post-adoption beliefs.
MIS Quarterly, 23(2), 183-213
Li, T. and Calantone, R. J. (1998). The impact of market knowledge competence on new
product advantage: conceptualization and empirical examination. Journal of
Marketing, 62(4), 13-29
M. G. Morris, A. Dillon (1997). How user perceptions influence software use. Decision
Support Systems, 58-65
Nunnally, J. C. (1978), Psychometric Theory, 2nded. , McGraw-Hill, New York, NY
R. E. Petty, D.T. Wegener, L.R. Fabrigar (1998). Attitude and attitude change. Annual Review
of Psychology, 48, 609-648
Ron Thompson, Deborah Compeau, Chris Higgins (2006). Intentions to use information
technologies: an integrative model. Journal of Organizational and End User
Computing, 18(3), 25-46
R. P. Bagozzi, F. D. Davis, P. R. Warshaw (1992). Development and test of a theory of
technological learning and usage. Human Relations, 45(7), 659-686
S. Taylor, P. Todd (1995). Assessing IT usage: the role of prior experience. MIS Quarterly,
19(4), 561-570
Steenkamp, J. -B. E. M. and van Trijp, H. C. M. (1991). The use of LISREL invalidating
marketing constructs. International Journal of Researching Marketing, 8(4),
283-299
T. C. Edwin Cheng , David Y. C. Lam, Andy C. L. Yeung (2006). Adoption of internet
banking: An empirical study in Hong Kong. Decision Support Systems, 42(3),
1558-1572
Tucker, L. R., and Lewis, C. (1973). The reliability coefficient for maximum likelihood
factor analysis. Psychometrika, 38, 1-10
Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. (2003). User acceptance of
information technology: Toward a unified view. MIS Quarterly, 27, 425-478
Walczuch, R., Lemmink, J. and Streukens, S. (2007). The effect of service employees'
technology readiness on technology acceptance. Information & Management, 44,
206-215
W. David Salisburg, Rodney A. Pearson, Allison W. Pearson, David W. Miller (2001).
Perceived security and world wide web purchase intention. Industrial Management
& Data systems, 101(4), 165-176
Wheaton, B. (1987). Assessment of fit in over-identified models with latent variables.
Sociological Methods and research, 16, 118-154
